受计算摄影学中边缘保持滤波器的启发，提出一种人脸图像光照归一化方法，能有效处理复杂光照条件下的人脸识别问题. 该方法采用加权最小均方边缘保持滤波器将亮度图像精确地分解为反射层与阴影层，根据视觉最优准则进行直方图匹配映射，获得视觉质量最优的光照归一化人脸图像. 对YALE-B 与CMU-PIE 人脸数据
库的识别结果表明该方法有效.

Inspired by the edge-preserving filter used in computational photography, we propose a method of illumination normalization for face recognition under varying lighting conditions. The brightness layer of a face image is accurately decomposed into a reflectance layer and a shading layer with a weighted least square filter. The histogram of the reflectance layer is remapped to that of sample images selected based on a visually optimal criterion to obtain an image with the best visual quality. Experimental results on the YALE-B and CMU-PIE face databases demonstrate effectiveness of the proposed method under varying illumination conditions.